A New Method Used for Traveling salesman problem Based on Discrete Artificial Bee Colony Algorithm

We propose a new method based on discrete Artificial Bee Colony algorithm (DABC) for traveling salesman problem(TSP). We redefine the searching strategy and transforming mechanism of leading bees, following bees and scout bees according to discrete variables. The transition of swarm role is based on ratio factor of definition. leading bees use 2-Opt operator and learning operator to accelerate the convergence speed and to search the neighborhood. The searching of following bees introduce tabu table to improve the local refinement ability of the algorithm. Scouts bees define exclusive operation to maintain the diversity of population, so it is better to balance the exploration and exploitation ability of the algorithm. Finally, the experimental results show that the new algorithm can find relatively satisfactory solution in a short time, and improve the efficiency of solving the TSP

SOX Genes and Cancer

Transcription factors play a critical role in regulating the gene expression programs that establish and maintain specific cell states in humans. Deregulation of these gene expression programs can lead to a broad range of diseases including cancer. SOX transcription factors are a conserved group of transcriptional regulators that mediates DNA binding by a highly conserved high-mobility group (HMG) domain. Numerous evidence has recently demonstrated that SOX transcription factors critically control cell fate and differentiation in major developmental processes, and that their upregulation may be important for cancer progression. In this review, we discuss recent advances in our understanding of the role of SOX genes in cancer

Mind Match: A Holistic App-Based Intervention for Post-Pandemic Adolescent Mental Health

The global COVID-19 pandemic and subsequent quarantine measures had profound implications for the mental well-being of adolescents. Studies have shown that adolescents who received MRIs post-pandemic demonstrated more severe mental health problems (Gotlib et al, 2021). As of 2021, more than 36% of DC youth who have depression did not receive any mental health care (NAMI, 2021). These statistics underscore the urgent need to address the mental health challenges faced by teenagers, particularly those from underrepresented backgrounds with limited access to mental health services (Mental health disparities: Diverse populations, 2017). In response to the downstream effects of COVID-19 on underrepresented adolescents\u27 mental health, our program, Mind Match, offers a comprehensive approach. It incorporates a mental health curriculum, an online certificate program, and a peer mentorship program. These components are facilitated through an innovative mental health monitoring application called Here 4 You, which also serves as a centralized platform for mental health support. Underclassmen engage in self-directed lessons, while upperclassmen can choose to become certified mentors for their peers. To monitor progress, our app will include a quarterly survey assessing depression, anxiety, social connectivity, and mental health knowledge. We will track academic performance, attendance, and in-school health resource utilization. With adequate funding, our app has the potential to mitigate the adverse effects of COVID-19 on adolescent mental health, making it an invaluable addition to school environments.https://hsrc.himmelfarb.gwu.edu/dchapp/1016/thumbnail.jp

SVH-B interacts directly with p53 and suppresses the transcriptional activity of p53

AbstractWe previously reported that inhibition of SVH-B, a specific splicing variant of SVH, results in apoptotic cell death. In this study, we reveal that this apoptosis may be dependent on the presence of p53. Co-immunoprecipitation and GST pull-down assays have demonstrated that SVH-B directly interacts with p53. In both BEL-7404 cells and p53-null Saos-2 cells transfected with a temperature-sensitive mutant of p53, V143A, ectopically expressed SVH-B suppresses the transcriptional activity of p53, and suppression of SVH by RNA interference increases the transcriptional activity of p53. Our results suggested the function of SVH-B in accelerating growth and inhibition of apoptosis is related to its inhibitory binding to p53

Effects of physical activity in child and adolescent depression and anxiety: role of inflammatory cytokines and stress-related peptide hormones

Depression and anxiety are the most common mental illnesses affecting children and adolescents, significantly harming their well-being. Research has shown that regular physical activity can promote cognitive, emotional, fundamental movement skills, and motor coordination, as a preventative measure for depression while reducing the suicide rate. However, little is known about the potential role of physical activity in adolescent depression and anxiety. The studies reviewed in this paper suggest that exercise can be an effective adjunctive treatment to improve depressive and anxiety symptoms in adolescents, although research on its neurobiological effects remains limited

ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models

AI generated content (AIGC) presents considerable challenge to educators around the world. Instructors need to be able to detect such text generated by large language models, either with the naked eye or with the help of some tools. There is also growing need to understand the lexical, syntactic and stylistic features of AIGC. To address these challenges in English language teaching, we first present ArguGPT, a balanced corpus of 4,038 argumentative essays generated by 7 GPT models in response to essay prompts from three sources: (1) in-class or homework exercises, (2) TOEFL and (3) GRE writing tasks. Machine-generated texts are paired with roughly equal number of human-written essays with three score levels matched in essay prompts. We then hire English instructors to distinguish machine essays from human ones. Results show that when first exposed to machine-generated essays, the instructors only have an accuracy of 61% in detecting them. But the number rises to 67% after one round of minimal self-training. Next, we perform linguistic analyses of these essays, which show that machines produce sentences with more complex syntactic structures while human essays tend to be lexically more complex. Finally, we test existing AIGC detectors and build our own detectors using SVMs and RoBERTa. Results suggest that a RoBERTa fine-tuned with the training set of ArguGPT achieves above 90% accuracy in both essay- and sentence-level classification. To the best of our knowledge, this is the first comprehensive analysis of argumentative essays produced by generative large language models. Machine-authored essays in ArguGPT and our models will be made publicly available at https://github.com/huhailinguist/ArguGP

Deciphering molecular details in the assembly of alpha-type carboxysome

Bacterial microcompartments (BMCs) are promising natural protein structures for applications that require the segregation of certain metabolic functions or molecular species in a defined microenvironment. To understand how endogenous cargos are packaged inside the protein shell is key for using BMCs as nano-scale reactors or delivery vesicles. In this report, we studied the encapsulation of RuBisCO into the α-type carboxysome from Halothiobacillus neapolitan. Our experimental data revealed that the CsoS2 scaffold proteins engage RuBisCO enzyme through an interaction with the small subunit (CbbS). In addition, the N domain of the large subunit (CbbL) of RuBisCO interacts with all shell proteins that can form the hexamers. The binding affinity between the N domain of CbbL and one of the major shell proteins, CsoS1C, is within the submicromolar range. The absence of the N domain also prevented the encapsulation of the rest of the RuBisCO subunits. Our findings complete the picture of how RuBisCOs are encapsulated into the α-type carboxysome and provide insights for future studies and engineering of carboxysome as a protein shell

PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization

Highly effective, task-specific prompts are often heavily engineered by experts to integrate detailed instructions and domain insights based on a deep understanding of both instincts of large language models (LLMs) and the intricacies of the target task. However, automating the generation of such expert-level prompts remains elusive. Existing prompt optimization methods tend to overlook the depth of domain knowledge and struggle to efficiently explore the vast space of expert-level prompts. Addressing this, we present PromptAgent, an optimization method that autonomously crafts prompts equivalent in quality to those handcrafted by experts. At its core, PromptAgent views prompt optimization as a strategic planning problem and employs a principled planning algorithm, rooted in Monte Carlo tree search, to strategically navigate the expert-level prompt space. Inspired by human-like trial-and-error exploration, PromptAgent induces precise expert-level insights and in-depth instructions by reflecting on model errors and generating constructive error feedback. Such a novel framework allows the agent to iteratively examine intermediate prompts (states), refine them based on error feedbacks (actions), simulate future rewards, and search for high-reward paths leading to expert prompts. We apply PromptAgent to 12 tasks spanning three practical domains: BIG-Bench Hard (BBH), as well as domain-specific and general NLP tasks, showing it significantly outperforms strong Chain-of-Thought and recent prompt optimization baselines. Extensive analyses emphasize its capability to craft expert-level, detailed, and domain-insightful prompts with great efficiency and generalizability.Comment: 34 pages, 10 figure